Novel feature selection methods to financial distress prediction

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107104

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107104

Title:	Novel feature selection methods to financial distress prediction
Authors:	梁德容;Lin, Fengyi;Liang, Deron;Yeh, Ching-Chiang;Huang, Jui-Chieh
Contributors:	資訊電機學院資訊工程學系
Keywords:	Algorithmics. Computability. Computer arithmetics;Algorithms;Applied sciences;Computer science;control theory;systems;Data processing. List processing. Character string processing;Design engineering;Empirical analysis;Exact sciences and technology;Expert systems;Feature selection;Finance;Financial distress prediction;Firm modelling;Genetic algorithm;Integrated prediction model;Mathematical models;Memory organisation. Data processing;Operational research and scientific management;Operational research. Management science;Portfolio theory;Searching;Semantics;Software;Theoretical computing;Wrappers
Date:	2014-04-01
Issue Date:	2026-04-23 13:56:42 (UTC+8)
Publisher:	Elsevier Ltd.;Amsterdam: Elsevier Ltd
Abstract:	摘要： •An integrated feature selection method is proposed to predict distress firms.•This method (HARC) embeds the experts’ knowledge with the wrapper method.•The financial ratios are categorized into seven classes using experts’ knowledge.•The prediction model based on HARC performs better than existing methods. Financially distressed prediction (FDP) has been a widely and continually studied topic in the field of corporate finance. One of the core problems to FDP is to design effective feature selection algorithms. In contrast to existing approaches, we propose an integrated approach to feature selection for the FDP problem that embeds expert knowledge with the wrapper method. The financial features are categorized into seven classes according to their financial semantics based on experts’ domain knowledge surveyed from literature. We then apply the wrapper method to search for “good” feature subsets consisting of top candidates from each feature class. For concept verification, we compare several scholars’ models as well as leading feature selection methods with the proposed method. Our empirical experiment indicates that the prediction model based on the feature set selected by the proposed method outperforms those models based on traditional feature selection methods in terms of prediction accuracy. 出版者： Amsterdam: Elsevier Ltd 出版日期： 2014-04-01 出處： Expert Systems with Applications, 2014-04, Vol.41 (5), p.2472-2483 版權： 2013 Elsevier Ltd 版權： 2015 INIST-CNRS 識別號： ISSN: 0957-4174 識別號： EISSN: 1873-6793 識別號： DOI: 10.1016/j.eswa.2013.09.047
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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